Biblio

Warnings happen a lot in real transmission networks. These warnings can affect people's lives. It is significant to analyze the alarm association rules in the network. Many algorithms can help solve this problem but not considering the actual physical significance. Therefore, in this study, we mine the association rules in warning weblogs based on a sequential mining algorithm (GSP) with topology structure. We define a topology constraint from network physical connection data. Under the topology constraint, network nodes have topology relation if they are directly connected or have a common adjacency node. In addition, due to the large amount of data, we implement the hash-tree search method to improve the mining efficiency. The theoretical solution is feasible and the simulation results verify our method. In simulation, the topology constraint improves the accuracy for 86%-96% and decreases the run time greatly at the same time. The hash-tree based mining results show that hash tree efficiency improvements are in 3-30% while the number of patterns remains unchanged. In conclusion, using our method can mine association rules efficiently and accurately in warning weblogs.

This paper proposes two adaptive Huffman coding hash tree algorithms to construct the hash tree of a file system. The algorithms are used to design the real-time proof of violation (PoV) scheme for the cloud storage service to achieve mutual non-repudiation between the user and the service provider. The PoV scheme can then generate cryptographic proofs once the service-level agreement (SLA) is violated. Based on adaptive Huffman coding, the proposed algorithms add hash tree nodes dynamically when a file is accessed for the first time. Every node keeps a count to reflect the frequency of occurrence of the associated file, and all nodes' counts and the tree structure are adjusted on-the-fly for every file access. This can significantly reduce the memory and computation overheads required by the PoV scheme. The file access patterns of the NCUCCWiki and the SNIA IOTTA datasets are used to evaluate the performance of the proposed algorithms. The algorithms are also compared with a related hash tree construction algorithm used in a PoV scheme, named ERA, to show their superiority in performance.